The following relates to the diagnostic arts, call center support arts, and related arts.
A customer support call center is a known approach for providing customer support. Conventionally, the call center is staffed by call center personnel who answer telephone calls from customers seeking assistance in using a product or service supported by the call center. In some instances (i.e. “call sessions”), the call center staffer may be able to diagnose the problem over the telephone and offer the customer a solution that may be carried out by the customer or performed remotely by the call center staffer (for example, reconfiguring a computer, printer or other customer device remotely via the Internet). In other instances, the call center staffer may determine that the problem cannot be solved directly by the customer or by remote operations, and may accordingly dispatch a service person to the customer site—in this case, the call center staffer endeavors to obtain relevant information so as to ensure the service person is suitably equipped to handle the matter and has sufficient allocated time for the on-site service call. In a variant resolution, the call center staffer may instruct the customer to bring the device or other item needing service to a store or other location staffed by the vendor. Other variants exist, which are not exhaustively cataloged here: for example, in some instances the call center staffer may provide assistance to a new customer, for example diagnosing the needs of the new customer and recommending (and possibly ordering) an appropriate product or service to satisfy the new customer.
Call centers can be costly to maintain; yet a poorly functioning call center provides poor customer experience which can lead to lost business, both immediately and over time. Throughput is also important—even if the call center resolves the customer's problem, if there is a long delay before the call is handled (e.g. the customer is placed “on hold” for a long time), then the overall customer experience may be graded poor. Accordingly there is substantial interest in providing a call center with low cost, high efficiency, and performance. To maximize performance it would be beneficial to staff the call center with highly skilled experts; however, this may introduce unacceptable costs, and/or there may not be enough such experts to adequately staff the call center, thus leading to long delays. On the other hand, cost and (possibly) throughput can be reduced by staffing the call center with less well trained staffers, but this may lead to reduced performance.
An automated call center support system can be provided to “bridge the gap” by providing call center staff with automated diagnostic support. In the context of customer care, a diagnostic engine can provide prediction and/or decision functionality. In a prediction task, the diagnostic system predicts a solution to propose to the customer by integrating domain knowledge and contextual information. In a decision task, the diagnostic system predicts a “next question” that the call center staffer can ask the customer in order to elicit useful information. The amount of automation provided by a call center support system can vary. At one end, a call center staffer handles the customer interaction, and the diagnostic engine is accessed via a computer to provide a most probable predicted solution, or a probative question recommendation. At the other end, in a fully automated call center support system the human staffer is replaced by an artificial intelligence (AI) agent supported by the diagnostic engine.
The diagnostic engine is typically a rules-based inference engine that applies facts and rules (i.e. the “knowledge base”) to predict a solution and/or recommend probative questions. A rules-based diagnostic engine is costly to design and maintain, as the set of rules needs to be initially generated (usually manually, constructed by expert design engineers or the like) and then kept up-to-date as the vendor's product (or service) line changes over time.
Diagnostic engines employing Bayesian or heuristic algorithms have also been contemplated. These approaches are less rigid than rule-based approaches, but still tend to follow a rigid sequence of symptom identification, root cause identification, and solution proposal, which can fail to fully leverage available information. Issues of rule maintenance when the number of rules is large can also arise.
Disclosed in the following are improved approaches.